Using LLMs to Evaluate Code

Software Engineering Institute (SEI) Webcast Series

A tartalmat a Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.

24d ago 1:02:10

MP4•Epizód kép

Finding and fixing weaknesses and vulnerabilities in source code has been an ongoing challenge. There is a lot of excitement about the ability of large language models (LLMs, e.g., GenAI) to produce and evaluate programs. One question related to this ability is: Do these systems help in practice? We ran experiments with various LLMs to see if they could correctly identify problems with source code or determine that there were no problems. This webcast will provide background on our methods and a summary of our results.

What Will Attendees Learn?

• how well LLMs can evaluate source code

• evolution of capability as new LLMs are released

• how to address potential gaps in capability

173 epizódok

#Tech #Cybersecurity #Securecoding #Softwarearchitecture #Softwaredevelopment #Softwareengineering #Carnegie Mellon University Software Engineering Institute #SEI Members of Technical Staff #Video #Podcasting Education